Built for the work, not the pitch

Clausebeam is a Washington DC-based team that came to contract AI from the legal side, not the AI side.

Why we built this

"We built Clausebeam because we've seen junior associates spend three weeks reading a deal room before the partners could even start their analysis. That bottleneck isn't skill, it's scale."

The problem is not that attorneys cannot read contracts. The problem is that no attorney can read forty-seven contracts in a single deal room before the weekend partner call. The first-pass diligence falls to the most junior associate in the room, and the senior attorneys reviewing that work are reviewing a read that could have been done in hours, not days.

Clausebeam is the analysis layer that precedes the attorney. It reads clause-by-clause for the specific language patterns that generate negotiation friction: indemnification that is too broad, liability caps that are below market, termination rights that run one way. The flagged memo goes to the attorney. They begin where they add value.

We are not a document management system, a CLM, or a legal chatbot. We do one thing: read each clause in a commercial agreement, compare it to market-norm language for that agreement type, and flag the deviations that carry real risk. That scope is intentional.

Catherine Liu, CEO & Co-Founder

2024
Founded in Washington, DC
34
clause types in detection model
DC
legal district, K Street NW
Legal-first
team background

The people behind the analysis

Catherine Liu, CEO and Co-Founder of Clausebeam
Catherine Liu
CEO & Co-Founder

Corporate attorney by training, NLP practitioner by necessity. Spent her early career in M&A practice in Washington DC, watching deal rooms arrive faster than first-pass reads could keep up. Founded Clausebeam in 2024 to solve the bottleneck she had worked around for years.

Co-Founder and CTO of Clausebeam
Arjun Mehta
CTO & Co-Founder

NLP researcher with a specialization in legal document structure and clause boundary detection. Before co-founding Clausebeam, built document classification and clause extraction systems for contract analytics tooling. The clause detection model is his work.

Head of Legal Products at Clausebeam
Sarah Okonkwo
Head of Legal Products

Former in-house counsel at a financial services firm, then legal ops lead at a growing technology company. Shapes Clausebeam's clause coverage decisions and flag logic, drawing on years of deciding which contract provisions actually required attorney time and which did not.

How we think about the problem

Three decisions we made early that govern what Clausebeam does and what it deliberately does not do.

1
Clause-level, not document-level analysis
Document-level summaries tell you what a contract is about. Clause-level analysis tells you where the risk is. Those are different problems. We solve the second one.
2
Deviation from market, not just pattern-matching
A clause isn't risky just because it's unusual. It's risky because it deviates from the market norm for that contract type in a way that shifts risk to your client. We score deviation, not novelty.
3
Output attorneys can use without re-reading
The flag report has to replace the first read, not add to the reading burden. Every flag includes the specific language, the deviation context, and what needs attorney eyes. Not a raw clause dump.

Washington, DC

1875 K Street NW, the legal district. We are a five-minute walk from the firms whose workflows we are trying to improve.

Building in Washington DC is a deliberate choice. This city is where federal contracting, regulatory practice, and M&A diligence run at high volume. The clause patterns in our detection model reflect the deal types that move through K Street law offices every week. We talk to the attorneys using this tool, and that proximity makes the product better.

Address
1875 K Street NW, Suite 700
Washington, DC 20006

Try it on a real contract.

Upload any commercial agreement and get a free clause-flag report. No credit card, no commitment.